[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126453-en":3,"doc-seo-126453-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126453,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Beyond Playing Positions - Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning","Soccer players are commonly categorized by playing position in both research and practice, yet its value for assessing physical match performance and guiding training remains uncertain. This study compares position-based grouping with unsupervised machine learning using match-specific running performance. Data from 40 young elite male players across two seasons were clustered via k-means, then sprint and endurance capacity, running metrics, within-subgroup variance, and between-subgroup standardized differences were evaluated.","VU Research Portal  \nBeyond Playing Positions  \nde Haan, Michel; van der Zwaard, Stephan; Sanders, Jurrit; Beek, Peter J. ; Jaspers, Richard T.  \npublished in  \nJournal of Sports Science & Medicine 2025  \nDOI (link to publisher)  \n10.52082/jssm.2025.565  \ndocument version  \nPublisher's PDF, also known as Version of record  \nLink to publication in VU Research Portal  \ncitation for published version (APA)  \nde Haan, M. , van der Zwaard, S. , Sanders, J. , Beek, P. J. , & Jaspers, R. T. (2025) . Beyond Playing Positions: Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning. Journal of Sports Science & Medicine, 24(3), 565-577 . [https://doi.org/10.52082/jssm.2025.565](https://doi.org/10.52082/jssm.2025.565)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nE-mail address:  \n[vuresearchportal.ub@vu.nl](vuresearchportal.ub@vu.nl)  \n[Download date: 19](Download date: 19) . Mar. 2026  \n©Journal of Sports Science and Medicine (2025) 24, 565-577 [http://www.jssm.org DOI:](http://www.jssm.org DOI:) [https://doi.org/10.52082/jssm.2025.565](https://doi.org/10.52082/jssm.2025.565)  \nResearch article  \nBeyond Playing Positions: Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning  \nMichel de Haan 1, Stephan van der Zwaard 1,2, Jurrit Sanders 3, Peter J. Beek 1 and Richard T. Jaspers 1 􀀍  \n1 Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Amsterdam, Netherlands; 2 Department of Cardiology, Amsterdam University Medical Center, location AMC, University of Amsterdam, Amsterdam, Netherlands; 3 PSV Eindhoven, Eindhoven, Netherlands  \nAbstract  \nSoccer players are frequently categorized by playing positions, both in the scientific literature and in practice. However, the utility of this approach in evaluating physical match performance and optimizing physical training programs remains unclear. This study compares the effectiveness of categorizing soccer players by their playing position versus using unsupervised machine learning based on match-specific running performance. Matchspecific running data were collected from 40 young elite male soccer players over two seasons. Thirty-one of these players completed a 20-meter sprint test and a maximal incremental treadmill test to measure maximal oxygen uptake. Players were categorized both by playing position and by subgroups derived through kmeans clustering based on match-specific running performance. Differences in sprint capacity, endurance capacity, and matchspecific running performance were compared between and within playing positions, as well as between and within clusters. The two categorization methods were further compared for variance within subgroups and standardized differences between subgroups for total distance (TD), low-intensity running (LIR), moderate-intensity running (MIR), high-intensity running (HIR), and sprint distance during matches. Match-specific running performance differed between playing positions, despite notable interindividual differences in running intensities within playing positions. Clustering based on match-specific running performance revealed less variance within groups (TD: P = 0.0","cbCaipPQa99u2Sxr","https://ap.wps.com/l/cbCaipPQa99u2Sxr","pdf",1198190,9,1,14,"English","en",105,"# Abstract\n# Introduction\n# Methods\n# Results\n## Categorization by playing position vs clustering\n## Sprint and endurance comparisons\n# Discussion\n# Conclusion\n# Key words","[{\"question\":\"What problem does the study address about soccer player categorization?\",\"answer\":\"It evaluates whether grouping soccer players by playing position effectively reflects physical match performance and supports optimizing training programs, compared with an unsupervised machine learning approach.\"},{\"question\":\"How were players categorized in the machine learning approach?\",\"answer\":\"Players’ match-specific running performance data were grouped using k-means clustering to form subgroups, which were then compared against position-based groups.\"},{\"question\":\"What were the main findings when comparing the two categorization methods?\",\"answer\":\"Clustering based on match-specific running performance produced less within-group variance and larger standardized differences between groups for several running metrics, and sprint speed differed between certain clusters but not between playing positions.\"}]","Beyond Playing Positions - Categorizing Soccer Players Based on Match-Specific Running Performance Using Machine Learning | PDF",1785905145,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"beyond-playing-positions-categorizing-soccer-players-based-on-match-specific-running-performance-using-machine-learning","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/beyond-playing-positions-categorizing-soccer-players-based-on-match-specific-running-performance-using-machine-learning/126453/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What problem does the study address about soccer player categorization?","Question",{"text":77,"@type":78},"It evaluates whether grouping soccer players by playing position effectively reflects physical match performance and supports optimizing training programs, compared with an unsupervised machine learning approach.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How were players categorized in the machine learning approach?",{"text":82,"@type":78},"Players’ match-specific running performance data were grouped using k-means clustering to form subgroups, which were then compared against position-based groups.",{"name":84,"@type":75,"acceptedAnswer":85},"What were the main findings when comparing the two categorization methods?",{"text":86,"@type":78},"Clustering based on match-specific running performance produced less within-group variance and larger standardized differences between groups for several running metrics, and sprint speed differed between certain clusters but not between playing positions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]